Artificial Intuition

The "Artificial Intuition" embedded in IntuScan is based on algorithms that apply to input of unstructured texts the aggregated comprehension by seasoned experts regarding texts of the same domain used in training.

IntuView has extended the concept of "data mining" to "meaning mining". The essence of this concept is to extract not only the prima facie identification of a word or string of words in a text, but to expand the identification to include implicit context-dependent and culture-dependent information or "hermeneutics" of the text. Thus, a word or quote in a text may "mean" something that even contradicts the ostensible definition of that text. IntuView relies on the identification of the general domain of the document and a sophisticated domain-specific ontology to extract the meaning beyond the text.

The human reader reaches “intuitive” conclusions - even by perfunctory reading - regarding the authorship and intent of a given text, inferring them from previous experience with similar texts or from extra-linguistic knowledge relevant to the text. Then as he accumulates more information through other features (statements, spelling, references) in the text, he either strengthens his confidence in the initial interpretation or changes it. These intuitive conclusions are part of what the Nobel Laureate, Prof. Daniel Kahneman called “fast thinking” – a judgment process that operates automatically and quickly, with little or no effort and no sense of voluntary control. An example of such "intuitive" understanding is as follows: A person who is accustomed to reading two newspapers may be shown two articles retyped with the same font, he would most likely identify the authorship by vocabulary, style and other features. Frequently, if asked, he would be hard put to explain his judgment. 

Extra-linguistic knowledge also provides us implicit information on a name’s bearer: gender, ethnicity, tribal and family relations, nicknames, status, religious affiliation and even age. We expect a person by the name of Nigel to be British and not American. We would also expect a person by the name of “Ethyl”, “Doris” or “Dorothy” to be an elderly woman. Indeed, the first two names are far more common in the UK than in the US and the two latter were more common in the 1930’s and decreased in popularity since.

The same words or allusions may have different meanings (polysemes) according to the target audience, the domain and the context of the surrounding text. The word "suit" in a legal text may refer to a legal petition, in a catalogue of a store; it would probably refer to a garment. It can also mean an effort to win the heart of a woman. References to famous historic events mean different things to the different sides.

The IntuView technology extracts such implicit meaning from a text or the hermeneutics of the text. It employs the relationship between lexical instances in the text and ontology - graph of unique language-independent concepts and entities that defines the precise meaning and features of each element and maps the semantic relationship between them.